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arXiv 2504.02232astro-ph.SRastro-ph.IM
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利用机器学习从中国空间站望远镜巡天中识别主序双星

Identify main-sequence binaries from the Chinese Space Station Telescope Survey with machine learning

Jia-jia Li, Jin-liang Wang, Kai-fan Ji, Chao Liu, Hai-liang Chen, Zhan-wen Han, Xue-fei Chen

AI总结:

本文提出一种基于机器学习的方法,利用中国空间站望远镜巡天数据区分单主序星与双主序双星,并结合探测效率与经验分布推断双星比例。

摘要:

双主序(MS)双星的统计性质对于双星演化和双星族群合成非常重要。为了获得这些性质,我们需要识别这些 MS 双星。在本文中,我们开发了一种方法,利用机器学习从中国空间站望远镜(CSST)巡天中区分单 MS 恒星和双 MS 双星。该方法在识别质量比在 0.20 到 0.80 之间的双星时可靠且高效,且不依赖于质量比分布。但由于对质量比小于 0.20 或大于 0.80 的双星探测效率较低,用该方法识别出的双星数量不能很好地近似原始样本中的双星数量。因此,我们利用该方法的探测效率和经验质量比分布改进了这一点,进而可以推断样本中的双星比例。一旦 CSST 数据可用,我们就可以用训练好的多层感知机模型识别 MS 双星,并推导样本的双星比例。

英文摘要:

The statistical properties of double main sequence (MS) binaries are very important for binary evolution and binary population synthesis. To obtain these properties, we need to identify these MS binaries. In this paper, we have developed a method to differentiate single MS stars from double MS binaries from the Chinese Space Station Telescope (CSST) Survey with machine learning. This method is reliable and efficient to identify binaries with mass ratios between 0.20 and 0.80, which is independent of the mass ratio distribution. But the number of binaries identified with this method is not a good approximation to the number of binaries in the original sample due to the low detection efficiency of binaries with mass ratios smaller than 0.20 or larger than 0.80. Therefore, we have improved this point by using the detection efficiencies of our method and an empirical mass ratio distribution and then can infer the binary fraction in the sample. Once the CSST data are available, we can identify MS binaries with our trained multi-layer perceptron model and derive the binary fraction of the sample.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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